Large-scale flood detection in the Pearl River basin based on GEE and time-series Sentinel-1 SAR images

被引：0

作者：

Zhao, Bofei ^{[1
]}

Sui, Haigang ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan, Peoples R China

来源：

14TH GEOINFORMATION FOR DISASTER MANAGEMENT, GI4DM 2022, VOL. 48-3 | 2022年

关键词：

SAR; GEE; Flood Detection; Sentinel-1; Large-scale; WATER BODY EXTRACTION; DEEP;

D O I：

10.5194/isprs-archives-XLVIII-3-W1-2022-87-2022

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Due to its wide scope, rapid development and high hazard, remote sensing images with a single time phase and local area coverage cannot meet the needs of large-scale flood detection. The problem of large-scale flooding emergency detection can be well solved by using Google Earth Engine (GEE) platform, which has the characteristics of fast data computing speed, easy image retrieval and rich remote sensing data. In this paper, we propose a method for flood detection using Sentienl-1 imagery based on the GEE platform, which can quickly accomplish a large scale flood detection. The method was validated in a flooding event that occurred around the Pearl River basin, China in June 2022. It shown that using the proposed method can not only meet the needs of basin-wide flood detection, but also extract information on the temporal dimension of the flood development status.

引用

页码：87 / 92

页数：6

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